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Real-Time Flood Monitoring and Prediction Using Integrative Remote Sensing and...
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Real-Time Flood Monitoring and Prediction Using Integrative Remote Sensing and AI

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 563

Special Issue Editors

Dr. Pietro Ceccato

E-Mail Website
Guest Editor
Copernicus Emergency Management Service, On-Demand Mapping, European Commission, Joint Research Centre, 21027 Ispra, Italy
Interests: floods monitoring; remote sensing and AI
Special Issues, Collections and Topics in MDPI journals
Pekel Jean-François

E-Mail Website
Guest Editor
Copernicus Emergency Management Service, On-Demand Mapping, European Commission, Joint Research Centre, 21027 Ispra, Italy
Interests: floods and water bodies monitoring; remote sensing and AI
Dr. Peter Salamon

E-Mail Website
Guest Editor
Copernicus Emergency Management Service, European Commission, Joint Research Centre, 21027 Ispra, Italy
Interests: floods monitoring and forecasting; hydrology; remote sensing

Special Issue Information

Dear Colleagues,

Climate change forecasters predict an increasing number of intense precipitation events with consequent flashes, riverine, and urban floods. An accurate and rapid mapping of these phenomena is a key component of effective emergency management and disaster risk reduction plans. Big data on Earth observation, such as the data acquired by the Copernicus programme, are providing unprecedented opportunities to help forecast and monitor floods.

Spatial information derived from remotely sensed data (e.g., satellites, aircrafts, and drones) or models associated with artificial intelligence is playing an increasingly important role in forecasting and monitoring the different types of floods in real time.

This Special Issue of Remote Sensing solicits papers that present innovative remotely sensed data, as well as hydrological models combined with artificial intelligence techniques to support monitoring and forecasting floods (especially in urban areas), in order to support efforts to better manage flood crises. 

Dr. Pietro Ceccato
Pekel Jean-François
Dr. Peter Salamon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • satellite image
  • artificial intelligence
  • flood depth
  • flood monitoring
  • flood forecasting
  • airplane/drone images
  • urban flood
  • hydrological models

Published Papers (1 paper)

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Research

19 pages, 7300 KiB  
Article
Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices
by Youn Joo Lee, Jun Young Hwang, Jiwon Park, Ho Gi Jung and Jae Kyu Suhr
Remote Sens. 2024, 16(13), 2358; https://doi.org/10.3390/rs16132358 - 27 Jun 2024
Viewed by 193
Abstract
Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, [...] Read more.
Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, this paper presents a deep neural network (DNN)-based real-time flood monitoring system for embedded Internet of Things (IoT) edge devices. The proposed system fuses long-wave infrared (LWIR) and RGB cameras to overcome a critical drawback of conventional RGB camera-based systems: severe performance deterioration at night. This system recognizes areas occupied by water using a DNN-based semantic segmentation network, whose input is a combination of RGB and LWIR images. Flood warning levels are predicted based on the water occupancy ratio calculated by the water segmentation result. The warning information is delivered to authorized personnel via a mobile message service. For real-time edge computing, the heavy semantic segmentation network is simplified by removing unimportant channels while maintaining performance by utilizing the network slimming technique. Experiments were conducted based on the dataset acquired from the sensor module with RGB and LWIR cameras installed in a flood-prone area. The results revealed that the proposed system successfully conducts water segmentation and correctly sends flood warning messages in both daytime and nighttime. Furthermore, all of the algorithms in this system were embedded on an embedded IoT edge device with a Qualcomm QCS610 System on Chip (SoC) and operated in real time. Full article
(This article belongs to the Special Issue Real-Time Flood Monitoring and Prediction Using Integrative Remote Sensing and AI)
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